Haplotype-based analysis methods for population genomics

描述（由申请人提供）： 该项目将开发一系列计算工具，利用基于单倍型的模型分析人口基因组学数据。这种工具的开发是特别重要的，因为测序的进步现在已经使得在整个染色体上收集序列数据成为常规。单倍型数据中存在的多位点连锁不平衡模式提供了有关群体遗传学中一系列重要过程的信息。利用单倍型中的信息在方法上是具有挑战性的，对于许多特定的问题，还不存在适当的分析工具。作为回应，我们的研究将在四个主要方向开发基于单倍型的模型。 首先，我们将开发基于单体型的模型，利用混合个体的遗传数据来推断重组率。关键的原则是混合个体的祖先转换点可以用来推断最近的重组事件。我们的工作将产生一个软件包，用于推断重组率的基础上，全基因组单核苷酸多态性数据，和一个单独的模拟包，用于生成数据，以测试该方法。一个关键的创新将是开发和测试这种方法的一个版本，可以处理多路（>2源人口）混合物。 第二，我们将使用单倍型知情的方法，以提高权力的复杂性状映射方法的基础上“进化和重新排序”的范例。功效的改善将来自于使用嵌入来自合并测序实验的原始读取数据中的单体型信息。我们将再次开发推理软件和模拟来测试推理方法。 第三，我们将调查净化到什么程度 选择塑造了人类群体中的单倍型多样性。期望的是，分离有害变体将显示减少的单倍型多样性，就像适应性变体一样。这种特征在很大程度上尚未被探索，我们将开发理论，经验和基于模拟的方法来确定这种属性是否存在以及如何使用它来推断人类群体遗传数据中的纯化选择强度。 最后，我们将推导出一种新形式的单倍型条件抽样分布（CSD）。CSD在群体遗传学中的应用已经取得了丰硕的成果，尽管该方法仍处于起步阶段。我们将开发一种方法，导致更准确的CSD。新的CSD还将为非平衡人口统计学和/或人口结构模型中计算单体型概率的扩展打开大门。 在整个项目中，将重点关注更广泛的人口基因组学社区的软件开发，以及克服基于单体型模型的计算和算法挑战。这些贡献对于推动群体遗传学进入基因组时代至关重要。项目相关性本项目将有助于基本工具包 人口遗传学家使用它从大型基因组数据集中提取信息，并将加强对一些具有实际意义的应用领域的研究。特别是，我们将开发工具，使研究人员能够测量重组，映射复杂的性状，并了解人类遗传变异的适应性后果。这些领域与疾病特征图谱、遗传疾病病因学和历史人口统计学有关。最后，我们希望开发的算法将是有用的，无论是直接或轻微的调整，以密切相关的问题超出了项目中的详细说明。作为一个例子，我们的算法在汇集的序列中的单倍型频率估计是密切相关的问题，确定血液DNA测序中的致病菌株的丰度。

DESCRIPTION (provided by applicant): This project will develop a series of computational tools that exploit the power of haplotype-based models for the analysis of population genomics data. The development of such tools is particularly important as advances in sequencing have now made it routine for sequence data to be gathered across full chromosomes. The multi-locus patterns of linkage disequilibrium that are present in haplotype data are informative about a range of important processes in population genetics. Leveraging the information in haplotypes is methodologically challenging, and for many specific problems the appropriate analysis tools do not yet exist. In response, our research will develop haplotype-based models in four major directions. First, we will develop haplotype-based models to infer recombination rates using genetic data from admixed individuals. The key principle is that ancestry switch points in admixed individuals can be used to infer recent recombination events. Our work will produce a software package for inference of recombination rates based on genome-wide single-nucleotide polymorphism data, and a separate simulation package for generating data with which to test the method. A key innovation will be developing and testing a version of this approach that can handle multi-way (>2 source population) admixtures. Second, we will use haplotype-informed approaches to improve the power of complex trait mapping approaches based on the "evolve and resequence" paradigm. The improvement in power will come from using haplotype information embedded in the raw read data from pooled sequencing experiments. Again we will develop both inference software and simulations to test the inference methods. Third, we will investigate to what extent purifying selection has shaped haplotype diversity in human populations. The expectation is that segregating deleterious variants will show reduced haplotype diversity, much as adaptive variants do. This signature has largely been unexplored and we will develop theoretical, empirical, and simulation-based approaches to establish whether this property exists and how it can be used to infer the strength of purifying selection in human population genetic data. Finally, we will derive a novel form of the conditional sampling distribution (CSD) for a haplotype. The application of CSDs in population genetics has been very fruitful, even though the approach is in its infancy. We will develop an approach that leads to a more accurate CSD. The new CSD will also open the door to extensions for computing haplotype probabilities in models with non-equilibrium demography and/or population structure. Throughout the project there will be an emphasis on software development for the broader population genomics community, and on overcoming computational and algorithmic challenges that arise commonly with haplotype-based models. The contributions are essential for pushing forward population genetics into the genomic era. Project Relevance This project will contribute to the basic toolkit population geneticists use to extract information from large genomic datasets and will enhance research on a number of applied areas with practical relevance. In particular we will develop tools that empower researchers to measure recombination, map complex traits, and understand the fitness consequences of human genetic variation. These areas are relevant to disease trait mapping, genetic disease etiology, and historical demography. Finally, we expect the algorithms developed will be useful either directly or with minor adjustment to closely related problems beyond those detailed in the project. As an example, our algorithms for haplotype frequency estimation in pooled sequences are closely related to problems for identifying the abundance of pathogenic strains in sequencing of blood DNA.